What is data quality?How to implement a data quality management strategy

Data is one of the primary sources of every business to create a competitive advantage.

It is now seen that since data is revolutionising across industries, businesses are facing constant pressure to innovate and increase profitability. Therefore, it is vital to design a framework for data management to reap rewards.

What is data quality?

Without an accurate understanding of what quality data looks like, companies, non-profit organisations, or other entities cannot have the highest data quality. There are many ways to define data quality, but all definitions have some things in common.

According to data quality experts, when the data meets the requirements of its intended use, the data is of high quality. In other words, when companies can use this data to communicate effectively with their constituents, determine customer needs, and find effective ways to serve their customer base, they know they have high-quality data.

This definition of data quality is broad enough to help companies with different products, markets, and missions to understand whether their data meets standards.

Why should you invest in data quality?

Our 2022 Global Data Management Research Report showed that 85 percent of organisations indicate that poor-quality contact data for customers negatively impacts their operational processes and efficiency and, in turn, hinders the chances of being flexible and agile. Poor-quality data has a ripple effect as do operational issues. When we consider the specific areas where bad data has an impact, our study uncovers wasted resources and additional costs (42%), negative affects to the customer experience (39%), and damage to the reliability of and trust in analytics (38%).

Investing in data quality management gives confidence in data validity to support strategic, tactical, and operational decision-making, irrespective of legacy or new data for businesses. The initiative to establish high-quality data can give you a competitive edge and turning point in your industry. For example, you can use your data to customise a marketing plan tailored for your targeted consumers to be ahead of the game. Read the customer case study from Bunches and explore how they attract new customers by delivering better-targeted marketing campaigns.

5 stages of data quality management

Data accuracy is the cornerstone to success—without it, organisations will continue to rely on that gut feeling and pass up the opportunity to make better and faster decisions for business growth. To create a robust quality data management process, we have created a best-practice guide that considers the 5 stages of data quality management:

1. Migrate data to consolidate silos

Departmental data siloes restrict organisational effectiveness. By consolidating all the data you hold into a single system, you can enjoy greater visibility and a holistic view of what your true database looks like, which gives you the opportunity to understand what gaps you currently have and how they can be addressed. You should develop a concrete plan for what data needs to be moved, where it’s going, and how you’ll get it there.

2. Validate your data

Once all data is migrated, the last thing you want to do is fill a brand-new database with bad data. Prevent the collection of poor data at its source by implementing real-time validation tools that check and validate data as it is entered across all touchpoints. It ensures the information you hold in your system – postal addresses, email addresses and phone numbers – are correct, consistent and up to date, saving you time and money. This is important because routinely sending to invalid addresses can hurt your reputation with mailbox providers and cause deliverability issues. It can also protect you against fraud. Sometimes customers do not want to give their true identity, it might be because they are trying to sign up to get a second chance at a special offer, or there may be more sinister reasons. Real-time validation can also check not just that an email or phone number is correct, but that it exists in the first place.

3. Enrich your data

Make your raw data more useful by expanding it with additional information. Combine first party data from internal sources with third party data from external sources. Enriched data instantly becomes fuller and more detailed making it more useable, insightful and valuable. Combining enriched data with an identity resolution solution can aid in the creation of the single customer view, allowing for better segmentation and targeting of customers by developing personalised relevant messaging and more engaging experiences.

4. Building and leveraging a single customer view

Implementing a Single Customer View is one of the fundamental success factors of a successful and competitive company. It demands cultural and systemic changes if the customer is to drive business priorities and marketing strategies. Identity Resolution can work quietly, consistently and regularly in the background. It is the key to knowing who your customers are in a way that is fuller and more holistic approach.

5. Routinely cleanse your data

Data needs to be cared for just like other key business assets. Regularly review of all the data within your database to either remove or update information that is incomplete, incorrect, improperly formatted, duplicated or irrelevant. Implementing an automated solution means this can be done quickly and easily, often in the background.

5 risks of untrustworthy data

A good data quality management plan often involves technology, employee participation, and support from data quality experts. The right resources and tools are vital to realise the best quality of data to run a profitable business that focuses on continued growth by eliminating the below risks:

1. Wasted time: Duplicate data is a pressing issue as it negatively impacts business with astronomical costs, low productivity, and ultimately brand image. Manually deleting duplicates is a significant waste of time and effort. These extra steps reduce employees’ productivity rather than encouraging them to focus on rewarding opportunities leading to innovation in business.
2. Missed opportunities: Across industries, many businesses leapfrog their competitors by leveraging data to unlock significant value. However, poor data quality due to inconsistencies in formats or error entries undermines business, potentially driving opportunities away to a competitor with advanced data governance process to capitalise.
3. Compliance issues: Data issues impose challenges to businesses on financial growth, productivity, and unreliable decision making. With the introduction of regulations like the General Data Protection Regulation (GDPR), organisations must be cautious in data collection, usage, and storage. Non-compliance with GDPR and security breaches impose stiff fines that affect reputation and interruptions to business operations. Whereas good data helps lower the risk of security issues and keeps you compliant with the latest data regulations.
4. Poor customer experience: Successful companies invest in providing their customers with a personalised brand experience as part of their core strategy. Inaccurate or outdated data can affect the rate of return on investment as it becomes challenging to analyse consumer behaviour, consumption patterns, and purchase history. It may affect the positioning of the right customers with the right products, leading to a waste of marketing efforts.
5. Unreliable decision-making: Poor data can lead to profound consequences. One of the biggest challenges is the inability to make accurate decisions. According to Experian’s 2021 research, 55% of business leaders lack confidence in their data assets. Hence, these leaders are reluctant to unlock insights to create value which affects day-to-day business decisions. Improving data quality provides assurance and trust into the insights generated for meeting business goals.

4 tips to start improving your data quality

Accessing the right tools, processes, and methods helps you design a sustainable data quality management plan. This plan requires a comprehensive mix of technology and people to embed the design into your strategy roadmap:

1. Determine data goals and quality metrics

A well-formulated data strategy is paramount for a business since it articulates its vision to store, use and manage data. This strategy defines the foundation to craft goals within the context of business and the process to execute them. The ambition to collect and manage a high quality of data from beginning to end will attribute to accurate decision making, regulatory compliance and high customer satisfaction.

2. Standardise and cleanse databases

Businesses are prone to lose opportunities when they have inconsistent, inaccurate, and outdated data. Data quality improvements are ideal to standardise and clean the data suitable to meet business objectives. The cleaned data is now the new oil for businesses to drill down to potentially create extraordinary value on products and services.

3. Fill data gaps

Data enrichment focuses on adding missing or extra attributes to the cleaned data resulting in rich insights of prospects and customers. The gaps can be further reduced by integrating data enrichment in real-time within CRM or other applications to append business data, location data, and demographics to your contacts. Enrichment supports in building Single Customer View to get an accurate view of your customers.

4. Use technology and resources for data quality management

Investing in the data quality journey by leveraging the right technology and resources is essential to gain insights and value on investment. Update your records regularly or automatically verify your customers’ contact data to remove manual tasks that hinder your employees and business.